1. Field
The present disclosure relates to a personalized recommendation method and system, and a computer-readable record medium, and more particularly, to a personalized recommendation method and system, and a computer-readable record medium capable of providing customer recommendation products using an analysis of a purchase cycle for each customer.
2. Background
A product recommendation service is widely being used as a main marketing means in the field of e-commerce.
Generally, when a customer visits an on-line market which is in an Internet or a mobile portal site, the on-line market recommends products in the form of a pop-up on a main window to the customer, or when the customer clicks specific products, the on-line market recommends related products to the customer. At this time, the recommended products may mostly be the most popular products, etc. in the on-line market. However, since such a product recommendation method recommends products en bloc to every customer without considering preference of each customer, there is a problem in that purchase effect of the recommended products is lowered.
Accordingly, the on-line market may provide a product recommendation service of recommending products in which a corresponding customer is likely to prefer by analyzing purchase behavior of the customer.
For example, products such as commodities, cosmetics, ingredients, etc. have a repeated and periodical purchase pattern. As such, a purchase cycle is an important factor for recommending products suitable for the customer.
The product recommendation service using the purchase cycle recommends products corresponding to the purchase cycle to the customer after calculating an average purchase cycle. However, when recommending the products using the average purchase cycle, there is a problem in which accuracy is lowered according to the product.
For example, when recommending an ice cream having a short purchase cycle in summer using the average purchase cycle, there is a problem of recommending the ice cream using the same purchase cycle even in winter having a long purchase cycle.
As such, when recommending the products using the average purchase cycle, there is a problem of recommending a corresponding product even when the customer does not need the corresponding product.
Meanwhile, the product recommendation service previously sets the purchase cycle of the corresponding product, and recommends the corresponding product every purchase cycle to the customer who purchased the corresponding product. However, in this case, since different purchase cycles between customers are not considered, there is a problem in which a purchase rate of recommendation product is lowered.
For example, when purchasing a sunblock, a first customer may purchase it every three months, a second customer may purchase it every six months, and a third customer may purchase it every year, but when setting the purchase cycle of the sunblock as the six-month cycle, there is a problem of recommending the sunblock every six-month cycle. Accordingly, there is a problem in which product recommendation for each customer cannot be precisely performed.